Promoting Diversity in Recommendation by Entropy Regularizer / 2698
Lijing Qin, Xiaoyan Zhu

We study the problem of diverse promoting recommendation task: selecting a subset of diverse items that can better predict a given user's preference. Recommendation techniques primarily based on user or item similarity can suffer from the risk that users cannot get expected information from the over-specified recommendation lists. In this paper, we propose an entropy regularizer to capture the notion of diversity. The entropy regularizer has good properties in that it satisfies monotonicity and submodularity, such that when we combine it with a modular rating set function, we get submodular objective function, which can be maximized approximately by efficient greedy algorithm, with provable constant factor guarantee of optimality. We apply our approach on the top-$K$ prediction problem and evaluate its performance on MovieLens data set, which is a standard database containing movie rating data collected from a popular online movie recommender system. We compare our model with the state-of-the-art recommendation algorithms. Our experiments show that the entropy regularizer effectively captures diversity and hence improves the performance of recommendation task.